5 papers
Scalable Pairwise Kernel Learning with Stochastic Vec Trick
Napsu Karmitsa, Tapio Pahikkala, Antti Airola
Pairwise learning is a specialized form of supervised learning that focuses on predicting outcomes for pairs of objects. In this work, we introduce SPaiK, a new scalable kernel lea…
A Comprehensive Guide to Differential Privacy: From Theory to User Expectations
Napsu Karmitsa, Antti Airola, Tapio Pahikkala +1
The increasing availability of personal data has enabled significant advances in fields such as machine learning, healthcare, and cybersecurity. However, this data abundance also r…
Inexact Limited Memory Bundle Method
Jenni Lampainen, Kaisa Joki, Napsu Karmitsa +1
Large-scale nonsmooth optimization problems arise in many real-world applications, but obtaining exact function and subgradient values for these problems may be computationally exp…
Clust-Splitter - an Efficient Nonsmooth Optimization-Based Algorithm for Clustering Large Datasets
Jenni Lampainen, Kaisa Joki, Napsu Karmitsa +1
Clustering is a fundamental task in data mining and machine learning, particularly for analyzing large-scale data. In this paper, we introduce Clust-Splitter, an efficient algorith…
Interaction Concordance Index: Performance Evaluation for Interaction Prediction Methods
Tapio Pahikkala, Riikka Numminen, Parisa Movahedi +2
Consider two sets of entities and their members' mutual affinity values, say drug-target affinities (DTA). Drugs and targets are said to interact in their effects on DTAs if drug's…